Many individuals with monogenic and idiopathic forms of autism spectrum disorder (ASD) exhibit larger brain volumes early in life. Cortical surface area hyper-expansion from 6 to 12 months of age precedes brain volume overgrowth from 12-24 months and predicts ASD diagnosis at 24 months of age. The underlying cellular and molecular mechanisms leading to this idiopathic ASD biomarker are unknown. Induced pluripotent stem cells (iPSCs) are an excellent model system to study cellular and molecular mechanisms regulating cortical surface area because they allow generation of neocortical neural progenitor cells and their neuronal progeny directly from well-characterized patients and controls. iPSC-derived neural cells also allow for cell-type specific measurements of gene regulation and gene expression, biological processes critical to inter-individual variability in post-natal cortical surface area and brain size. In this proposal, we will study molecular phenotypes in iPSC-derived neural progenitors and neurons from extensively phenotyped participants of the largest longitudinal neuroimaging study of infants at high risk for autism. Individuals selected for participation in this proposal have previously undergone (1) longitudinal neuroimaging at 6, 12, and 24 months of age, (2) extensive behavioral assessments, as well as (3) rare and common variant genotyping. We will leverage this unique, deeply characterized clinical sample to relate individual in vitro measures of neocortical proliferation and neurogenesis to in vivo measures of infant brain structure and ASD severity. First, we will generate iPSCs, differentiated neocortical progenitors, and neurons from individuals with ASD (n=5) and matched controls (n=5) who have been deeply phenotyped since infancy. Then, we will identify molecular signatures of ASD- associated cortical brain overgrowth during neural progenitor proliferation and neurogenesis using multiple high-throughput genomic methods. Finally, we will relate in vitro signatures of neural development to in vivo measures of infant brain structure and behavior. An important advantage of our study is that we are able to not only identify gene modules and regulatory elements associated with risk for ASD, but we are also able to evaluate if those genes and regulatory elements can predict in vivo cortical surface area trajectories during infant development that precede the subsequent emergence of autistic behavior. This represents a unique opportunity as no similar cohort exists that phenotyped infants from high risk families and acquired cortical surface area phenotypes (both cross-sectional and longitudinal) during the first two years of life, the period when symptoms of autism are first emerging. Data and analyses generated in this proposal will lead to well- defined cellular and molecular mechanisms during neocortical neurogenesis associated with cortical surface area overgrowth in autism. These mechanisms can directly address questions of molecular convergence prior to symptom onset and can serve as a basis for therapeutic development in idiopathic autism.
Many individuals with autism spectrum disorder exhibit larger brain volumes early in life. We will generate and study neural cells from individuals with ASD and matched controls who have been profiled with behavioral assessments and neuroimaging since infancy in order to identify cellular and molecular mechanisms leading to early brain overgrowth. Insight into these mechanisms will further understanding of disease pathogenesis, delineate convergent biological pathways within a heterogeneous disorder, and will inform targeted early therapeutic intervention.
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| Ngattai Lam, Prince D; Belhomme, Gaetan; Ferrall, Jessica et al. (2018) TRAFIC: Fiber Tract Classification Using Deep Learning. Proc SPIE Int Soc Opt Eng 10574: |
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